A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties
Identification of favorable biophysical properties for protein therapeutics as part of
developability assessment is a crucial part of the preclinical development process.
Successful prediction of such properties and bioassay results from calculated in silico
features has potential to reduce the time and cost of delivering clinical-grade material to
patients, but nevertheless has remained an ongoing challenge to the field. Here, we
demonstrate an automated and flexible machine learning workflow designed to compare …
developability assessment is a crucial part of the preclinical development process.
Successful prediction of such properties and bioassay results from calculated in silico
features has potential to reduce the time and cost of delivering clinical-grade material to
patients, but nevertheless has remained an ongoing challenge to the field. Here, we
demonstrate an automated and flexible machine learning workflow designed to compare …
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